Automatic registration method for three-dimensional point cloud data

Abstract

The invention discloses an automatic registration method for three-dimensional point cloud data. The method comprises the steps that two point clouds to be registered are sampled to obtain feature points, rotation invariant feature factors of the feature points are calculated, and the rotation invariant feature factors of the feature points in the two point clouds are subjected to matching search to obtain an initial corresponding relation between the feature points; then, a random sample consensus algorithm is adopted to judge and remove mismatching points existing in an initial matching point set to obtain an optimized feature point corresponding relation, and a rough rigid transformation relation between the two point clouds is obtained through calculation to realize rough registration; a rigid transformation consistency detection algorithm is provided, a local coordinate system transformation relation between the matching feature points is utilized to perform binding detection on the rough registration result, and verification of the correctness of the rough registration result is completed; and an ICP algorithm is adopted to optimize the rigid transformation relation between the point cloud data to realize automatic precise registration of the point clouds finally.

Description

technical field [0001] The invention belongs to the field of three-dimensional measurement, and more specifically relates to an automatic registration method for three-dimensional point cloud data. Background technique [0002] The 3D model is of great significance in the fields of industrial inspection, cultural relics protection, biomedicine and so on. With the development of 3D measurement technology, the processing technology of point cloud model has become a research hotspot in recent years. An important step in point cloud processing is to unify the point cloud data obtained from different viewpoints in the same scene into the same coordinate system, that is, point cloud registration. [0003] The point cloud registration methods are mainly divided into the following categories: (1) Manual registration: Manually select the corresponding points between two point clouds to achieve merging. This method requires manual intervention, and the effect of merging often depends...

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Problems solved by technology

[0005] Existing point cloud registration methods usually use a combination of coarse registration and precise registration. Among them, coarse registration generally finds corresponding points by calculating the characteristics of points, and estimates the initial position relationship between the two point clouds; The registration criterion is to use ICP and its improved algorithm to further optimize the rough registration results to achieve precise registration of point cloud data; And the interference factors such as point cloud density changes are relatively sensitive, and a large number of mismatching points are prone to appear, which reduces the accuracy and stability of registration.

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